Many current processes for manufacturing microelectronic devices, and the like, entail the fabrication of stacks of thin films having compositions that must be controlled to satisfy exacting standards and extremely tight tolerances. For example, fluorinated silica glass (FSG) films are used as dielectric layers in semiconductor integrated circuits (IC""s); because the fluorine constituent serves to reduce the dielectric constant of the layer, and thereby to improve circuit speed and reduce cross-talk between wires, it is important to control its concentration so as to optimize the electrical, chemical, and other properties of the layer. Similarly, the dopant concentrations in boron- and phosphorus-doped silica glass (BPSG) films, which are used as intermetal dielectrics, are quite critical and must be monitored and controlled carefully to maintain high yields in production, and the same is true of many of the newer materials that are now being employed as thin films, which materials tend to be compounds with complex chemical compositions and interfaces that must be monitored and controlled.
It is now also common for integrated circuits to include complex submicron geometric structures. For example, dynamic random access memory (DRAM) devices are typically formed with deep, tapered trenches etched into the silicon to thereby provide larger area capacitors without increasing the chip surface area utilized. The sidewall taper angle and depth of such trenches must be controlled carefully to maintain yield; moreover, the formed trenches may themselves be filled subsequently with materials such as dielectrics and polysilicon, giving rise to the need for yet further process monitoring and control capability.
In addition to the foregoing, compositionally graded structures are often employed in thin film manufacturing; dopants are typically diffused or implanted into semiconductor devices to provide diffuse composition profiles; and silicon on insulator (SOI) wafers are commonly fabricated by forming a buried oxide layer with a graded composition profile, which must be sharpened by thermal annealing. Controlling the depth profiles of such structures is of critical importance.
In all of the processes referred to, as well as in others that will be evident to those skilled in the art, it is necessary to measure the properties and characteristics of at least one layer of interest. Typically, and for any of a number of reasons, such measurements cannot presently be performed directly on the product wafers: i.e., product wafers with patterns, and multilayered film stacks, are often too complex for practical data analysis; measurements are often either destructive to the sample or are such that they pose a significant risk of contamination or defect introduction; and current techniques, carried out in the visible and ultraviolet wavelength ranges, are not sufficiently sensitive to enable accurate measurement of crucial layer properties, such as composition and carrier concentration. In any event, nondestructive measurement techniques will usually be preferred, largely because they enable the use of in-line process control methodologies without undue consumption of expensive, non-product test wafers.
Accordingly, broad objects of the present invention are to provide a novel method and apparatus for measuring and controlling the composition and other properties of thin films, in accordance with which at least certain of the limitations and deficiencies of current techniques and procedures, as described herein, are minimized, avoided and/or overcome.
It has now been found that certain of the foregoing and related objects of the invention are attained by the provision of a method for estimating at least one parameter of a sample, determined from the dielectric function of a material of which at least one layer of the sample consists. The method comprises the steps:
(a) providing a sample comprised of at least one layer and having a substantially specular surface;
(b) defining an optical model of the sample along a direction perpendicular to its surface and based upon reflectance values, the xe2x80x9cat least one layerxe2x80x9d being defmed in the model by a thickness value and, for each of a multiplicity of wavelengths in the infrared spectral region, by a dielectric function value;
(c) providing a training set consisting of measured values of the xe2x80x9cat least one parameterxe2x80x9d and an associated dielectric function, the measured values being obtained from a multiplicity of samples selected to represent a range of values of the at least one parameter;
(d) determining from the training set a predictive mathematical relationship between the at least one parameter and the associated dielectric function, so as to enable prediction of the at least one parameter from input values of dielectric function;
(e) irradiating the specular surface of the sample with infrared radiation, including the multiplicity of wavelengths referred to in step (b), and obtaining a measured reflectance spectrum composed of values obtained over the multiplicity of wavelengths;
(f) simulating a reflectance spectrum from the optical model at the multiplicity of wavelengths using various values of the dielectric function calculated from assumed dielectric function descriptors and a value of the thickness of the at least one layer, and computing the various values of the dielectric function descriptors so as to minimize the difference between the simulated reflectance spectrum and the measured reflectance spectrum, thereby determining an optimized dielectric function value for the at least one layer at the multiplicity of wavelengths; and
(g) calculating the value of the at least one parameter using the optimized dielectric function value and the predictive mathematical relationship.
In certain preferred embodiments a pattern of variation derived from the training set is utilized so as to constrain the number of the descriptors required to describe the dielectric function. The values of dielectric function used may be parametrized as weighted linear superpositions of vectors determined to span the space of dielectric functions derived from the training set, with the descriptors being the coefficients of the vectors. At least one of the vectors will desirably be determined through a multivariate statistical regression of the set of dielectric functions measured in creating the training set. The predictive mathematical relationship may for example be determined through a multivariate statistical regression of the training set; it may be determined employing a neural network algorithm calibrated with the training set; or it may be determined by establishing a library of dielectric function values with associated values of the at least one parameter, organized in the form of a look-up table which is accessed to determine the parameter from the optimized values, and access may include the additional step of interpolating between elements thereof. The predictive mathematical relationship may be established between the at least one parameter and spectral features derived from the dielectric function of the training set. The spectral feature may be at least one characteristic of at least one peak observed in the training set dielectric function, such a characteristic typically being the intensity, position, height or width of the at least one peak. In certain instances the thickness value will be varied in step (f) as well as using the computed values of the dielectric function descriptors.
The at least one parameter calculated by the present method may represent the species and/or the concentration of at least one chemical constituent of the material of the at least one layer. In particular, the parameter may be the concentration of fluorine atoms within the material, the concentration of hydroxyl groups within a dielectric matrix, the concentration of water molecules within a dielectric matrix, the concentration of hydrogen atoms within a dielectric matrix, or the concentrations of at least one of boron, phosphorus, and germanium in the at least one layer. When the at least one layer contains barium, strontium and/or titanium atoms, the parameter may be the concentration thereof. Additionally, the parameter value calculated may represent the stress in the at least one layer, the density of crystal defects therein, or a porosity characteristic thereof, which characteristic may describe the pore size distribution or the total fractional pore volume within the layer. Furthermore, the value determined may be characteristic of a lithography process, in instances in which the layer of interest comprises a lithographic resist layer, with the parameter representing the exposure dose of the lithographic resist layer, a critical dimension obtained after completing a lithography process step on the sample, or a measure of the sidewall profile obtained following such a process step. In instances in which the one layer is a compound semiconductor composed of at least three chemical elements, the parameter may be representative of the relative ratios of the elements, such elements generally being selected from the group consisting of Si, Ge, Al, Ga, As, N, P, In, C, Sb, Zn, Hg, Cd, B, and Te. Finally, the at least one parameter may be representative of the electrical dielectric constant of the. analyzed layer.
Other objects of the invention are attained by the provision of apparatus for estimating at least one parameter of a sample, determined from the dielectric function of a material of which at least one layer of the sample consists. The apparatus comprises means for irradiating a surface of a sample with infrared radiation, including each of multiplicity of wavelengths in the infrared spectral region, and for obtaining a measured reflectance spectrum composed of values obtained over the multiplicity of wavelengths; and electronic data processing means. The data processing means of the apparatus is programmed to:
(a) define an optical model of the sample along a direction perpendicular to a surface thereof and based upon reflectance values, the at least one layer of the sample being defined in the model by a thickness value and, for each of a multiplicity of wavelengths in the infrared spectral region, by a dielectric function value;
(b) provide a training set consisting of measured values of the at least one parameter and an associated dielectric function, the measured values being obtained from a multiplicity of samples selected to represent a range of values of the at least one parameter;
(c) determine from the training set a predictive mathematical relationship between the at least one parameter and the associated dielectric function, so as to enable prediction of the at least one parameter from input values of dielectric function;
(d) simulate a reflectance spectrum from the optical model at the multiplicity of wavelengths using values of the dielectric function calculated from various dielectric function descriptors and a value of the thickness of the at least one layer, and to compute the values of the dielectric function descriptors so as to minimize the difference between the simulated reflectance spectrum and the measured reflectance spectrum, thereby determining an optimized dielectric function value for the at least one layer at the multiplicity of wave-lengths; and
(e) calculate the value of the at least one parameter using the optimized dielectric function value and the predictive mathematical relationship.
As a practical matter, the invention relates to the measurement and control of layer and interface properties of thin films fabricated during the manufacture of microelectronic devices, solar conversion devices, magnetic storage devices, and the like, enabling the control of such properties and of such manufacturing operations. It provides a new approach for measuring layers deposited in thin-film structures of both present and also anticipated constructions, and it enables measurements of layers in relatively complex structures, such as multilayered film stacks. The method does not require calibration to be performed on the same measurement system as that which is present in a tool by which the method is to be implemented, and calibration samples can be relatively simple; for example, they may comprise a set of films covering a range of composition, all deposited on inexpensive, unpatterned silicon substrates. As will be appreciated, once the calibration has been performed using the calibration samples, actual measurements can be carried out on metallic substrates, multilayered configurations, or other more complicated structures.
The method and apparatus of the invention exploit a number of unique capabilities associated with infrared measurement techniques. In particular, the optical constants of most materials encode a great deal of chemical composition information that is unavailable in the UV and visible ranges. The longer wavelengths of infrared light also allow specular measurements on many patterned samples, whereas the scattering and diffraction of UV and visible probing beams, caused by patterns and layer roughness, often preclude accurate specular measurements. In addition, the infrared dielectric function is a complex spectral quantity which encodes information about the absorption and index of refraction of a material, as a function of optical frequency, and most materials have characteristic dielectric function signatures, in the infrared, that are related to their composition. The present invention enables the extraction of information indicative of the composition of a given layer, by computing its dielectric function from optical measurements, and at the same time eliminates confounding effects of other layers and substrate materials present in the structure; chemometric compositional analysis of the dielectric function can then be performed effectively.
One advantage of the instant approach resides in the correlation that it provides between the composition of a material and its dielectric function, which correlation does not depend upon details of the measurement geometry employed, e.g., the angle of incidence or the polarization of the probe beam. Optical properties, such as reflectance and transmit-tance, do on the other hand depend strongly upon the characteristics of the probe beam.
It is know that infrared reflectometry can be employed to extract layer composition (see J. E. Franke, T. M. Niemczyk, and D. M. Haaland, xe2x80x9cInfrared spectroscopic techniques for quantitative characterization of dielectric thin films on silicon wafersxe2x80x9d Spectrochimica Acta. Wol. 50A, No. 10. Pp 1687-1723, 1994 Elsevier Science Ltd. U.K.). However, in the methodology described the chemometric compositional analysis algorithm is applied directly to the reflectance, which not only limits the range of allowable sample thicknesses to that which is provided in the calibration sample set, but also requires the training set to constitute a relatively large number of sample thicknesses (as well as compositions) to accommodate measurements over widely varying thicknesses. Thus, the method of Haaland (and other current methods) permits measurements to be made only on samples comprised of a substrate and film stack structure that is essentially identical to that which constitutes the calibration training set. By applying chemometric analysis to the dielectric function (DF), on the other hand, which can be extracted from the reflectance in accordance with the present invention, the foregoing shortcomings are eliminated and calibration of the analysis system is enabled using much simpler, and much smaller, training sets.
More specifically, because the DF deconvolution removes from the chemometric model the influences of other variables, such as are attributable to the substrate and to the thickness of the layers, the training set need not span a wide range of film thicknesses and it can constitute relatively few members. Also, because the deconvolution process eliminates the effects of other layers that may be present thin films deposited in more complicated structures, such as on metal or dielectric underlayers, can be analyzed using an established calibration model; i.e., no new set of training samples is required. And finally, because the DF deconvolution eliminates any influence that the angle of probe beam incidence might have upon the spectral data obtained, measurements can be performed using a measurement geometry that is radically different from that which is employed to generate the calibration data.